Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory383.9 KiB
Average record size in memory393.1 B

Variable types

DateTime1
Text2
Categorical2
Numeric6

Alerts

Timestamp has unique valuesUnique
Source IP has unique valuesUnique
Destination IP has unique valuesUnique

Reproduction

Analysis started2024-02-27 01:18:42.226344
Analysis finished2024-02-27 01:18:47.353390
Duration5.13 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Timestamp
Date

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2024-01-12 00:00:00
Maximum2024-02-22 15:00:00
2024-02-26T20:18:47.476629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:47.646845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Source IP
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.7 KiB
2024-02-26T20:18:47.893792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.176
Min length9

Characters and Unicode

Total characters13176
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row132.30.200.186
2nd row67.0.50.150
3rd row148.102.62.138
4th row196.92.142.223
5th row92.164.155.104
ValueCountFrequency (%)
132.30.200.186 1
 
0.1%
205.251.40.139 1
 
0.1%
177.230.254.62 1
 
0.1%
67.56.69.11 1
 
0.1%
148.102.62.138 1
 
0.1%
196.92.142.223 1
 
0.1%
92.164.155.104 1
 
0.1%
77.151.138.154 1
 
0.1%
108.23.207.21 1
 
0.1%
54.220.222.231 1
 
0.1%
Other values (990) 990
99.0%
2024-02-26T20:18:48.287884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.8%
1 2482
18.8%
2 1681
12.8%
3 847
 
6.4%
4 832
 
6.3%
5 805
 
6.1%
0 729
 
5.5%
9 720
 
5.5%
8 700
 
5.3%
6 700
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10176
77.2%
Other Punctuation 3000
 
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2482
24.4%
2 1681
16.5%
3 847
 
8.3%
4 832
 
8.2%
5 805
 
7.9%
0 729
 
7.2%
9 720
 
7.1%
8 700
 
6.9%
6 700
 
6.9%
7 680
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 3000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3000
22.8%
1 2482
18.8%
2 1681
12.8%
3 847
 
6.4%
4 832
 
6.3%
5 805
 
6.1%
0 729
 
5.5%
9 720
 
5.5%
8 700
 
5.3%
6 700
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3000
22.8%
1 2482
18.8%
2 1681
12.8%
3 847
 
6.4%
4 832
 
6.3%
5 805
 
6.1%
0 729
 
5.5%
9 720
 
5.5%
8 700
 
5.3%
6 700
 
5.3%

Destination IP
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.7 KiB
2024-02-26T20:18:48.527012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.214
Min length9

Characters and Unicode

Total characters13214
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row62.88.113.46
2nd row183.40.208.165
3rd row2.18.3.105
4th row220.126.1.114
5th row58.79.254.171
ValueCountFrequency (%)
62.88.113.46 1
 
0.1%
133.170.137.141 1
 
0.1%
49.186.108.14 1
 
0.1%
148.139.213.202 1
 
0.1%
2.18.3.105 1
 
0.1%
220.126.1.114 1
 
0.1%
58.79.254.171 1
 
0.1%
43.171.114.161 1
 
0.1%
59.105.202.172 1
 
0.1%
15.206.80.8 1
 
0.1%
Other values (990) 990
99.0%
2024-02-26T20:18:48.948568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.7%
1 2575
19.5%
2 1587
12.0%
3 892
 
6.8%
4 819
 
6.2%
5 791
 
6.0%
6 743
 
5.6%
0 717
 
5.4%
8 707
 
5.4%
9 698
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10214
77.3%
Other Punctuation 3000
 
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2575
25.2%
2 1587
15.5%
3 892
 
8.7%
4 819
 
8.0%
5 791
 
7.7%
6 743
 
7.3%
0 717
 
7.0%
8 707
 
6.9%
9 698
 
6.8%
7 685
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 3000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13214
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3000
22.7%
1 2575
19.5%
2 1587
12.0%
3 892
 
6.8%
4 819
 
6.2%
5 791
 
6.0%
6 743
 
5.6%
0 717
 
5.4%
8 707
 
5.4%
9 698
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3000
22.7%
1 2575
19.5%
2 1587
12.0%
3 892
 
6.8%
4 819
 
6.2%
5 791
 
6.0%
6 743
 
5.6%
0 717
 
5.4%
8 707
 
5.4%
9 698
 
5.3%

Attack Type
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
DDoS
174 
Ransomware
169 
Phishing
169 
SQL Injection
167 
Malware
162 

Length

Max length14
Median length10
Mean length9.269
Min length4

Characters and Unicode

Total characters9269
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRansomware
2nd rowMalware
3rd rowSQL Injection
4th rowInsider Threat
5th rowRansomware

Common Values

ValueCountFrequency (%)
DDoS 174
17.4%
Ransomware 169
16.9%
Phishing 169
16.9%
SQL Injection 167
16.7%
Malware 162
16.2%
Insider Threat 159
15.9%

Length

2024-02-26T20:18:49.114501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T20:18:49.250554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ddos 174
13.1%
ransomware 169
12.7%
phishing 169
12.7%
sql 167
12.6%
injection 167
12.6%
malware 162
12.2%
insider 159
12.0%
threat 159
12.0%

Most occurring characters

ValueCountFrequency (%)
n 831
 
9.0%
a 821
 
8.9%
e 816
 
8.8%
i 664
 
7.2%
r 649
 
7.0%
o 510
 
5.5%
h 497
 
5.4%
s 497
 
5.4%
D 348
 
3.8%
S 341
 
3.7%
Other values (16) 3295
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6935
74.8%
Uppercase Letter 2008
 
21.7%
Space Separator 326
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 831
12.0%
a 821
11.8%
e 816
11.8%
i 664
9.6%
r 649
9.4%
o 510
7.4%
h 497
7.2%
s 497
7.2%
w 331
 
4.8%
t 326
 
4.7%
Other values (6) 993
14.3%
Uppercase Letter
ValueCountFrequency (%)
D 348
17.3%
S 341
17.0%
I 326
16.2%
R 169
8.4%
P 169
8.4%
Q 167
8.3%
L 167
8.3%
M 162
8.1%
T 159
7.9%
Space Separator
ValueCountFrequency (%)
326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8943
96.5%
Common 326
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 831
 
9.3%
a 821
 
9.2%
e 816
 
9.1%
i 664
 
7.4%
r 649
 
7.3%
o 510
 
5.7%
h 497
 
5.6%
s 497
 
5.6%
D 348
 
3.9%
S 341
 
3.8%
Other values (15) 2969
33.2%
Common
ValueCountFrequency (%)
326
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 831
 
9.0%
a 821
 
8.9%
e 816
 
8.8%
i 664
 
7.2%
r 649
 
7.0%
o 510
 
5.5%
h 497
 
5.4%
s 497
 
5.4%
D 348
 
3.8%
S 341
 
3.7%
Other values (16) 3295
35.5%

Severity
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
Critical
265 
Medium
253 
High
252 
Low
230 

Length

Max length8
Median length6
Mean length5.336
Min length3

Characters and Unicode

Total characters5336
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowCritical
3rd rowHigh
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
Critical 265
26.5%
Medium 253
25.3%
High 252
25.2%
Low 230
23.0%

Length

2024-02-26T20:18:49.393878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T20:18:49.508178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
critical 265
26.5%
medium 253
25.3%
high 252
25.2%
low 230
23.0%

Most occurring characters

ValueCountFrequency (%)
i 1035
19.4%
C 265
 
5.0%
r 265
 
5.0%
t 265
 
5.0%
c 265
 
5.0%
a 265
 
5.0%
l 265
 
5.0%
m 253
 
4.7%
u 253
 
4.7%
d 253
 
4.7%
Other values (8) 1952
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4336
81.3%
Uppercase Letter 1000
 
18.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1035
23.9%
r 265
 
6.1%
t 265
 
6.1%
c 265
 
6.1%
a 265
 
6.1%
l 265
 
6.1%
m 253
 
5.8%
u 253
 
5.8%
d 253
 
5.8%
e 253
 
5.8%
Other values (4) 964
22.2%
Uppercase Letter
ValueCountFrequency (%)
C 265
26.5%
M 253
25.3%
H 252
25.2%
L 230
23.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5336
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1035
19.4%
C 265
 
5.0%
r 265
 
5.0%
t 265
 
5.0%
c 265
 
5.0%
a 265
 
5.0%
l 265
 
5.0%
m 253
 
4.7%
u 253
 
4.7%
d 253
 
4.7%
Other values (8) 1952
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1035
19.4%
C 265
 
5.0%
r 265
 
5.0%
t 265
 
5.0%
c 265
 
5.0%
a 265
 
5.0%
l 265
 
5.0%
m 253
 
4.7%
u 253
 
4.7%
d 253
 
4.7%
Other values (8) 1952
36.6%

Attempt Count
Real number (ℝ)

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.025
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-26T20:18:49.623758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5107843
Coefficient of variation (CV)0.54970417
Kurtosis-1.1811198
Mean10.025
Median Absolute Deviation (MAD)5
Skewness0.0032340051
Sum10025
Variance30.368744
MonotonicityNot monotonic
2024-02-26T20:18:49.771363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19 63
 
6.3%
16 62
 
6.2%
10 60
 
6.0%
8 58
 
5.8%
6 57
 
5.7%
9 56
 
5.6%
2 56
 
5.6%
12 56
 
5.6%
11 55
 
5.5%
1 55
 
5.5%
Other values (9) 422
42.2%
ValueCountFrequency (%)
1 55
5.5%
2 56
5.6%
3 53
5.3%
4 47
4.7%
5 43
4.3%
6 57
5.7%
7 46
4.6%
8 58
5.8%
9 56
5.6%
10 60
6.0%
ValueCountFrequency (%)
19 63
6.3%
18 45
4.5%
17 54
5.4%
16 62
6.2%
15 40
4.0%
14 48
4.8%
13 46
4.6%
12 56
5.6%
11 55
5.5%
10 60
6.0%

Data Volume (MB)
Real number (ℝ)

Distinct628
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.045
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-26T20:18:49.927776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile50
Q1268.75
median504.5
Q3728
95-th percentile954.1
Maximum999
Range997
Interquartile range (IQR)459.25

Descriptive statistics

Standard deviation282.69606
Coefficient of variation (CV)0.56647408
Kurtosis-1.0870347
Mean499.045
Median Absolute Deviation (MAD)230.5
Skewness0.011353094
Sum499045
Variance79917.062
MonotonicityNot monotonic
2024-02-26T20:18:50.097922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 6
 
0.6%
556 6
 
0.6%
328 6
 
0.6%
805 5
 
0.5%
34 5
 
0.5%
537 5
 
0.5%
28 4
 
0.4%
287 4
 
0.4%
57 4
 
0.4%
534 4
 
0.4%
Other values (618) 951
95.1%
ValueCountFrequency (%)
2 1
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
15 2
0.2%
16 3
0.3%
17 1
 
0.1%
18 1
 
0.1%
ValueCountFrequency (%)
999 1
 
0.1%
998 1
 
0.1%
997 4
0.4%
996 2
0.2%
994 2
0.2%
992 2
0.2%
990 2
0.2%
989 2
0.2%
988 2
0.2%
987 1
 
0.1%

Source Latitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.8186
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative182
Negative (%)18.2%
Memory size7.9 KiB
2024-02-26T20:18:50.252307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.6937
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)14.589

Descriptive statistics

Standard deviation30.418318
Coefficient of variation (CV)1.0201122
Kurtosis0.50415566
Mean29.8186
Median Absolute Deviation (MAD)6.6606
Skewness-1.4726232
Sum29818.601
Variance925.27405
MonotonicityNot monotonic
2024-02-26T20:18:50.426059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
55.9533 80
 
8.0%
51.5074 76
 
7.6%
43.651 72
 
7.2%
41.8781 71
 
7.1%
40.7128 70
 
7.0%
34.6937 70
 
7.0%
49.2827 70
 
7.0%
35.6895 65
 
6.5%
-27.4698 64
 
6.4%
35.0116 61
 
6.1%
Other values (5) 301
30.1%
ValueCountFrequency (%)
-37.8136 59
5.9%
-33.8688 59
5.9%
-27.4698 64
6.4%
34.0522 61
6.1%
34.6937 70
7.0%
35.0116 61
6.1%
35.6895 65
6.5%
40.7128 70
7.0%
41.8781 71
7.1%
43.651 72
7.2%
ValueCountFrequency (%)
55.9533 80
8.0%
53.4808 61
6.1%
51.5074 76
7.6%
49.2827 70
7.0%
45.5017 61
6.1%
43.651 72
7.2%
41.8781 71
7.1%
40.7128 70
7.0%
35.6895 65
6.5%
35.0116 61
6.1%

Source Longitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.279138
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative622
Negative (%)62.2%
Memory size7.9 KiB
2024-02-26T20:18:50.564184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3135.7681
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)215.1151

Descriptive statistics

Standard deviation105.54072
Coefficient of variation (CV)6.4831885
Kurtosis-1.6363272
Mean16.279138
Median Absolute Deviation (MAD)116.0011
Skewness0.17181621
Sum16279.138
Variance11138.844
MonotonicityNot monotonic
2024-02-26T20:18:50.691857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-3.1883 80
 
8.0%
-0.1278 76
 
7.6%
-79.347 72
 
7.2%
-87.6298 71
 
7.1%
-74.006 70
 
7.0%
135.5023 70
 
7.0%
-123.1207 70
 
7.0%
139.6917 65
 
6.5%
153.0251 64
 
6.4%
135.7681 61
 
6.1%
Other values (5) 301
30.1%
ValueCountFrequency (%)
-123.1207 70
7.0%
-118.2437 61
6.1%
-87.6298 71
7.1%
-79.347 72
7.2%
-74.006 70
7.0%
-73.5673 61
6.1%
-3.1883 80
8.0%
-2.2426 61
6.1%
-0.1278 76
7.6%
135.5023 70
7.0%
ValueCountFrequency (%)
153.0251 64
6.4%
151.2093 59
5.9%
144.9631 59
5.9%
139.6917 65
6.5%
135.7681 61
6.1%
135.5023 70
7.0%
-0.1278 76
7.6%
-2.2426 61
6.1%
-3.1883 80
8.0%
-73.5673 61
6.1%

Destination Latitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.189468
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative211
Negative (%)21.1%
Memory size7.9 KiB
2024-02-26T20:18:50.811903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation31.827113
Coefficient of variation (CV)1.1705677
Kurtosis-0.10744825
Mean27.189468
Median Absolute Deviation (MAD)6.6606
Skewness-1.2735108
Sum27189.468
Variance1012.9651
MonotonicityNot monotonic
2024-02-26T20:18:51.112234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
35.0116 80
 
8.0%
35.6895 77
 
7.7%
-33.8688 73
 
7.3%
-27.4698 73
 
7.3%
40.7128 71
 
7.1%
53.4808 69
 
6.9%
43.651 69
 
6.9%
-37.8136 65
 
6.5%
34.6937 64
 
6.4%
55.9533 63
 
6.3%
Other values (5) 296
29.6%
ValueCountFrequency (%)
-37.8136 65
6.5%
-33.8688 73
7.3%
-27.4698 73
7.3%
34.0522 52
5.2%
34.6937 64
6.4%
35.0116 80
8.0%
35.6895 77
7.7%
40.7128 71
7.1%
41.8781 62
6.2%
43.651 69
6.9%
ValueCountFrequency (%)
55.9533 63
6.3%
53.4808 69
6.9%
51.5074 61
6.1%
49.2827 59
5.9%
45.5017 62
6.2%
43.651 69
6.9%
41.8781 62
6.2%
40.7128 71
7.1%
35.6895 77
7.7%
35.0116 80
8.0%

Destination Longitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.421788
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative568
Negative (%)56.8%
Memory size7.9 KiB
2024-02-26T20:18:51.222683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-74.006
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)213.6977

Descriptive statistics

Standard deviation106.83405
Coefficient of variation (CV)3.895955
Kurtosis-1.7152703
Mean27.421788
Median Absolute Deviation (MAD)116.0011
Skewness-0.0025088246
Sum27421.788
Variance11413.514
MonotonicityNot monotonic
2024-02-26T20:18:51.347145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
135.7681 80
 
8.0%
139.6917 77
 
7.7%
151.2093 73
 
7.3%
153.0251 73
 
7.3%
-74.006 71
 
7.1%
-2.2426 69
 
6.9%
-79.347 69
 
6.9%
144.9631 65
 
6.5%
135.5023 64
 
6.4%
-3.1883 63
 
6.3%
Other values (5) 296
29.6%
ValueCountFrequency (%)
-123.1207 59
5.9%
-118.2437 52
5.2%
-87.6298 62
6.2%
-79.347 69
6.9%
-74.006 71
7.1%
-73.5673 62
6.2%
-3.1883 63
6.3%
-2.2426 69
6.9%
-0.1278 61
6.1%
135.5023 64
6.4%
ValueCountFrequency (%)
153.0251 73
7.3%
151.2093 73
7.3%
144.9631 65
6.5%
139.6917 77
7.7%
135.7681 80
8.0%
135.5023 64
6.4%
-0.1278 61
6.1%
-2.2426 69
6.9%
-3.1883 63
6.3%
-73.5673 62
6.2%

Interactions

2024-02-26T20:18:46.493146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:43.328860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:43.955310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.680755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.313796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.900733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.594115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:43.435010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.064134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.792873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.416725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.998739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.698261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:43.539628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.167037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.899421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.511390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.091996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.798934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:43.638513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.268823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.004454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.608216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.188665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.900680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:43.740531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.490227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.108363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.701048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.300681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:47.002219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:43.844795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:44.585022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.213268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:45.803622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:18:46.396626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-02-26T20:18:47.150684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-26T20:18:47.296824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
02024-01-12 00:00:00132.30.200.18662.88.113.46RansomwareMedium920835.6895139.691751.5074-0.1278
12024-01-12 01:00:0067.0.50.150183.40.208.165MalwareCritical142035.0116135.768155.9533-3.1883
22024-01-12 02:00:00148.102.62.1382.18.3.105SQL InjectionHigh16934.0522-118.243753.4808-2.2426
32024-01-12 03:00:00196.92.142.223220.126.1.114Insider ThreatLow5391-37.8136144.963153.4808-2.2426
42024-01-12 04:00:0092.164.155.10458.79.254.171RansomwareHigh1089935.6895139.691734.6937135.5023
52024-01-12 05:00:0077.151.138.15443.171.114.161MalwareLow2605-37.8136144.9631-33.8688151.2093
62024-01-12 06:00:00108.23.207.2159.105.202.172DDoSHigh47840.7128-74.006041.8781-87.6298
72024-01-12 07:00:0054.220.222.23115.206.80.8MalwareLow1139335.0116135.768135.6895139.6917
82024-01-12 08:00:0077.58.136.10143.43.235.50MalwareCritical367434.0522-118.2437-27.4698153.0251
92024-01-12 09:00:00197.94.227.215123.6.225.254DDoSLow116235.0116135.768153.4808-2.2426
TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
9902024-02-22 06:00:00181.238.34.6485.232.63.8SQL InjectionCritical2956-27.4698153.0251-37.8136144.9631
9912024-02-22 07:00:0063.222.125.2861.116.144.89Insider ThreatHigh666743.6510-79.347049.2827-123.1207
9922024-02-22 08:00:007.137.184.15968.142.123.216SQL InjectionCritical732745.5017-73.567335.0116135.7681
9932024-02-22 09:00:0086.72.160.220165.139.37.106SQL InjectionLow1086035.6895139.691734.6937135.5023
9942024-02-22 10:00:00112.50.64.22667.185.143.90RansomwareHigh1541434.6937135.502345.5017-73.5673
9952024-02-22 11:00:00189.200.38.243223.111.88.59MalwareMedium139643.6510-79.347040.7128-74.0060
9962024-02-22 12:00:00202.188.20.180164.225.37.119DDoSMedium192853.4808-2.242640.7128-74.0060
9972024-02-22 13:00:00130.153.229.34126.10.147.89RansomwareMedium1654340.7128-74.006051.5074-0.1278
9982024-02-22 14:00:00176.138.7.2034.96.101.91DDoSCritical694055.9533-3.188353.4808-2.2426
9992024-02-22 15:00:00185.219.1.126207.106.198.88RansomwareCritical254743.6510-79.347040.7128-74.0060